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The artificial neural network (ANN), which is capable of noise removal and data complexity reduction, has been regarded as one of outstanding intermediaries in the two-stage clustering procedures. Various ANN-based two-stage clustering procedures have been individually proposed; however, the performance among those methods has not been examined yet. In this study, a preliminary comparative analysis is conducted in four benchmark data-sets and a real-world market data-set, which are used to simulate various conditions for evaluation purposes. The experiment results suggest that high-accuracy self-organizing feature map can potentially improve the effectiveness of decision-making.